As a consequence of the No Free Lunch Theorems for optimisation [1]}, if an optimisation algorithm is tuned to improve its performance on one class of problems it will most likely perform worse on other problems [2]}. It is important, therefore, when benchmarking algorithms, to use problems that match the target problem class as closely as possible. In addition, to develop algorithms that perform well in general or are able to adapt to new scenarios, a wide range of different benchmark suites is needed for experimental algorithm design.
